Data Profile for This Category
Typical entities in this category are carbonated beverage, juice, tea, functional beverage, and packaged drinking water factories. Daily documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspections, and finished product inspections. Data sources cover water source and raw material testing, formula systems, online sugar and acidity monitoring, filling equipment, visual inspection, and LIMS systems. Data updates occur per batch, per shift, or via continuous collection. Document structure includes static formula parameters and batch labels, plus dynamic real-time detection values and equipment operation logs. Fields include sugar content, acidity, feeding amount, batch number, and shelf life.
Constraints for Vector Models and Indexes
Multi-source, high-frequency updated data requires indexes to support incremental triggers. This avoids the long time cost of full indexing. Differences in document structure require chunking rules that balance information integrity for long-text formula sheets and short-text inspection records. Continuously collected real-time data has many repeated batch-associated fields. Unify field weights during vector encoding to avoid redundant information interfering with matching accuracy. High batch density from high-speed filling requires index sharding and recall efficiency to match batch review real-time needs. This ensures fast response for compliance review workflows.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Balances information coverage for long-text formula sheets and short-text inspection records of this category, avoids splitting critical parameters |
| Overlap Length | 100–150 characters | Preserves cross-chunk batch numbers and associated feeding information, prevents matching failure caused by chunk breakage |
| Similarity Threshold | 0.75–0.85 | Adapts to the high-precision requirements of compliance review for this category, filters low-matching irrelevant documents |
defaultToken | 4096 | Adapts to the basic encoding window of FastGPT v4.17.0, accommodates core information of single-chunk documents |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Single Search Limit | Top 10 entries | Covers associated documents across multiple links of this category, ensures compliance review does not miss critical records |
Three Common Configuration Mistakes
Mistake 1
Phenomenon: After uploading batch documents in chunk mode, the interface continuously displays "Indexing" and ultimately returns no data. Cause: Index trigger rules are not configured with batch grouping. Continuously collected data for this category is not correctly associated with batch labels, causing the index to fail to complete grouping verification.
Mistake 2
Phenomenon: When initiating a review query, the last split index chunk fails to complete encoding. Cause: maxToken is set too small. Long batch sterilization or filling records exceed the encoding window, causing index interruption.
Mistake 3
Phenomenon: The relevance of search recall results is generally low, and cannot match associated documents of the target batch. Cause: Chunk length is set too short. Associated information between formula sheets and feeding records is split, causing vector encoding to lose critical association logic.
How to Verify Proper Configuration
- Upload a single complete formula sheet document, check if the chunked text retains all feeding parameters and batch identifiers.
- Trigger a batch upload of single-shift water treatment records, verify that the index progress completes within a reasonable time frame.
- Initiate a compliance review query for a specific batch, confirm that the recall results include all associated document types for that batch.
- Adjust the similarity threshold, verify that the matching accuracy of returned results meets the compliance requirements of business review.
The values provided are common starting points and should be measured against the reader's own samples.